Assimilation of FY-3D MWTS-II Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors. The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally, without considering the three-dimensional distribution of clouds. Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach. In this study, the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2 (MWTS-II) onboard the Fengyun-3D, which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters. Cycling data assimilation and forecasting experiments for Typhoons Lekima (2019) and Mitag (2019) are carried out. Compared with the control experiment, the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%. The quality of the additional MWTS-II radiance data is close to the clear-sky data. The case studies show that the average root-mean-square errors (RMSE) of prognostic variables are reduced by 1.7% in the upper troposphere, leading to an average reduction of 4.53% in typhoon track forecasts. The detailed diagnoses of Typhoon Lekima (2019) further show that the additional MWTS-II radiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation, thus providing more precise structures. This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.